Potential Diagnosis of Cancerous Cells Through Utilising Optical Spectroscopy
Cancer is responsible for an estimated 9.6 million deaths in 2018. Deaths from cancer worldwide are projected to reach over 13 million in 2030. Thus, developing a device that has the capability to solve today’s toughest global challenge is crucial by utilizing a simple yet robust approach - “SEEING THE UNSEEABLE” through bold innovation. Although removing cancer is much more effective than either radiation or chemotherapy, when unseen residual cancer cells remain, they could grow back into tumour overtime. The reoccurrence of cancer contributes to a greater risk of death. Hence, launching a system that is able to distinguish between the cancerous cell and normal cell is ultimately essential to make sure no cancer is left behind during surgery. This robust optical system is established with quantitative approach by exploring the integration of an algorithm into the developed software. The end result of this device has the capability to provide users an accurate numerical pH value. The developed system is integrated with the smart IoT gateway capability whereby this powerful analytical device is incorporated with the real-time monitoring, data transformation and data analyzer. Harnessing the power of technology lets us fight cancer better. Each time a pathologist analyzes tissue after operation, it can take up 2 to 3 days because the tissue has to be frozen, thinly sliced, and stained so it can be viewed under the microscope during the process of biopsy. Thus, it is crucial to invent this Surgeons’ VisionMetric device which has an IoT-based microcontroller that is capable of providing real-time numerical value on-site.
Improving Particle Classification In Wimp Dark Matter Detection Using Neural Networks
In all experiments for detection of WIMP dark matter, it is essential to develop a classifier that can distinguish potential WIMP events from background radiation. Most often, clas- sifiers are developed manually, via physical modeling and empirical optimization. This is problematic for two reasons: it takes a great deal of time and effort away from developing the experiment, and the resulting classifiers often perform suboptimally (which means that a greater amount of expensive run time is required to obtain a confident experimental result). Machine learning has the potential to automate this and accelerate experimentation, and also to detect patterns that humans cannot. However, two major challenges, which are shared among several dark matter experiments, stand in the way: impure calibration data, which hinders training of models, and unpredictable physical dynamics within the detector itself. My objective was to develop a set of machine learning techniques that address these two problems, and thus more efficiently generate highly accurate classifiers. I was able to obtain raw data for two dark matter experiments which exhibit these challenges: the PICO-60 bubble chamber [2], and the DEAP-3600 liquid argon scintillator [1]. For each experiment, I developed and compared three general-purpose algorithms intended to resolve its inherent challenge (impurity and unpredictable dynamics, respectively). In PICO-60, background alpha and WIMP-like neutron calibration datasets are used for training; however, there is an impurity of 10% alphas in the neutron set. While a conventional classifier was developed (and is believed to be 100% accurate), machine learning in the form of a supervised neural network (NN) has also been previously explored, because of the benefits of automation. Unfortunately, it achieved a mean accuracy of only 80.2% – not usable as a practical replacement for conventional methods in future iterations of the experiment. In DEAP-3600, photons are absorbed by a wavelength shifting medium and re-emitted in an unpredictable direction, before being detected by one of 255 photomultiplier tubes (PMTs) around the spherical detector. The randomness severely limits the accuracy of conventional classifiers; in a simulation, the best so far removes 99.6% of alpha background, while also (undesirably) removing 91.0% of WIMP events. Because of physical limitations, simulated data is used for calibration, with 30 real-world experimental events available for testing. I have written a research paper [11] about my work on PICO-60, which has been approved by the PICO collaboration and pre-published at https://arxiv.org/abs/1811.11308. It is currently undergoing peer review for publication in Computer Physics Communications. All PICO researchers are listed on my paper for their work on the original PICO-60 experi- ment. They did not contribute to this study; I completed and documented it independently.
圓周上跳躍回歸問題之研究
圓周上相異n個點，將圓周分割成n段弧，每次每個點沿逆時針方向變換成與下一點所成弧之中點，若某點經m次變換後回到初始點，則m的最小值以及m的所有可能值為何？我們發現，m的最小值為n+2。更進一步發現，m的充要條件為m≧n+2且m≠kn-1, kn, kn+1，其中k為正奇數。接著，我們將問題一般化，圓周上相異n個點，沿逆時針方向變換成與下一點所成弧之p:q處，若某點經m次變換後回到初始點，則m的最小值以及m的所有可能值為何？我們發現，若p, q∈N，(p,q)=1，當變換次數r足夠大時，此n個點的位置會收斂至圓周上n等分點，同時，此n個點會在變換T=n(p+q)/(n,p)次後再次收斂至相同的位置。在這篇研究中，我們推導出任意點Pi變換r次後的點之位置坐標Ai(r)的一般式，不失一般性，我們針對P0求出A0(r)的最小極端值Lr與最大極端值Ur，在變換次數r足夠大時，透過觀察Lr與Ur對應到圓周上的收斂位置所形成的區間是否涵蓋原點，可預期P0變換r次後可否回歸。此外，我們也針對n個點具特殊初始位置座標來研究其回歸性質。
Removal of Nutrients by Chlorella Vulgaris Microalgae in Bandar Abbas Municipal Wastewater
The entry of nutrients into the environment can cause the creation of eutrophication of aquatic ecosystems. One of the methods of removing nutrients from effluents is the use of algae. Algal purification is a new and inexpensive technology for this purpose. The present study investigated the rate of cell growth and nutrient removal of urban wastewater in Bandar Abbas in winter 2020 by the Chlorella vulgaris microalgae in the phycolab of Fisheries Research. Treatments with different dilutions (0%, 25%, 50% and 75%) were prepared; in addition, specific growth rate, cell density and removal efficiency of phosphate, nitrate, nitrite were examined during a 14 day period with initial constant density (1×10⁶ cells / ml ) of microalgae. The results indicated that 0% and 75% dilution had the highest and lowest cell densities (8.675×10⁶ and 56.633×10⁶), respectively; moreover, they had the specific growth rate (0.166 and 0.311). Furthermore, there was a significant difference between them (P≥ 0.05). The highest nitrate and nitrite removal efficiencies were -40.75 and -79.84 in effluent dilution of 50%; in addition, the lowest were 1.26 and -40.26 in dilution of 75% and 25% respectively. Phosphate had the highest removal efficiency at 0% dilution with a mean of -79.65 that showed a significant difference with the lowest at 25% dilution (P≥ 0.05). Therefore, high or low levels of nutrients can affect the removal efficiency and growth rate of microalgae.
Using EEG Neuro-Feedback technology to control a prosthetic hand
Unaffordable healthcare and excessive plastic waste are both alarming issues that are plaguing modern society. Recent studies conducted by the World Health Organisation (WHO) report that about 15% of the world's population suffer from a form of disability, of which 50% of the demographic cannot afford adequate health care. Furthermore, 8 million metric tons of plastic annually enter our oceans (apart from the 150 metric tons that currently circulate our oceans!). In conjunction to the global plastic pollution crisis, unnecessary invasive surgery is currently being done on amputees. Many of these desperate patients are forced to pay exorbitant prices in order to live a normal life with bionic prosthetics. The solution… Project Limbs - an EEG, 3D printed prosthetic printed from recycled plastic. Signal processors will be implemented to build an affordable and easy-to-use ‘mind controlled prosthetic hand’, that requires no invasive surgery.